Detection

Object detection is a task that involves identifying the location and class of objects in an image or video stream.

The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.

Tip

YOLOv8 detection models have no suffix and are the default YOLOv8 models, i.e. yolov8n.pt and are pretrained on COCO.

Models

Train

Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Train the model
model.train(data="coco128.yaml", epochs=100, imgsz=640)

yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640

Val

Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the model retains it's training data and arguments as model attributes.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Validate the model
metrics = model.val()  # no arguments needed, dataset and settings remembered
metrics.box.map    # map50-95
metrics.box.map50  # map50
metrics.box.map75  # map75
metrics.box.maps   # a list contains map50-95 of each category

yolo detect val model=yolov8n.pt  # val official model
yolo detect val model=path/to/best.pt  # val custom model

Predict

Use a trained YOLOv8n model to run predictions on images.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image

yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"  # predict with official model
yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model

Read more details of predict in our Predict page.

Export

Export a YOLOv8n model to a different format like ONNX, CoreML, etc.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom trained

# Export the model
model.export(format="onnx")

yolo export model=yolov8n.pt format=onnx  # export official model
yolo export model=path/to/best.pt format=onnx  # export custom trained model

Available YOLOv8 export formats include:

Format	`format=`	Model
PyTorch	-	`yolov8n.pt`
TorchScript	`torchscript`	`yolov8n.torchscript`
ONNX	`onnx`	`yolov8n.onnx`
OpenVINO	`openvino`	`yolov8n_openvino_model/`
TensorRT	`engine`	`yolov8n.engine`
CoreML	`coreml`	`yolov8n.mlmodel`
TensorFlow SavedModel	`saved_model`	`yolov8n_saved_model/`
TensorFlow GraphDef	`pb`	`yolov8n.pb`
TensorFlow Lite	`tflite`	`yolov8n.tflite`
TensorFlow Edge TPU	`edgetpu`	`yolov8n_edgetpu.tflite`
TensorFlow.js	`tfjs`	`yolov8n_web_model/`
PaddlePaddle	`paddle`	`yolov8n_paddle_model/`